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Research Assistant Deep Learning Jobs in Maryland

Quidient

Staff Deep Learning Engineer

Columbia, MD · On-site

$185K - $235K/yr

  • Medical

  • Life

  • Retirement

  • PTO

Overview We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an ...

Whiterabbit.ai

Train on a dedicated high-performance compute cluster specialized for deep learning research * Work with doctors and healthcare professionals to identify serious problems and leverage their domain ...

Whiterabbit.ai

Train on a dedicated high-performance compute cluster specialized for deep learning research * Work with doctors and healthcare professionals to identify serious problems and leverage their domain ...

Whiterabbit.ai

Train on a dedicated high-performance compute cluster specialized for deep learning research * Work with doctors and healthcare professionals to identify serious problems and leverage their domain ...

Whiterabbit.ai

Train on a dedicated high-performance compute cluster specialized for deep learning research * Work with doctors and healthcare professionals to identify serious problems and leverage their domain ...

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Research Assistant Deep Learning information

What is the difference between Research Assistant Deep Learning vs Research Assistant Machine Learning?

AspectResearch Assistant Deep LearningResearch Assistant Machine Learning
Required CredentialsBachelor's or Master's in Computer Science, Data Science, or related fields; knowledge of neural networksBachelor's or Master's in Computer Science, Data Science, or related fields; foundational ML knowledge
Work EnvironmentResearch labs, universities, tech companies focusing on AI and neural networksResearch labs, universities, tech companies working on various ML algorithms
Employer & Industry UsageAI research, deep learning projects, neural network developmentGeneral machine learning applications, data analysis, predictive modeling

Research Assistant Deep Learning specializes in neural networks and AI-focused projects, while Research Assistant Machine Learning covers a broader range of algorithms and data analysis tasks. Both roles require similar educational backgrounds but differ in technical focus and application areas.

What is a research assistant deep learning?

Research Assistant Deep Learning jobs involve supporting research projects focused on artificial intelligence, specifically within the field of deep learning. These roles typically require assisting with data collection, preprocessing, running machine learning experiments, and analyzing results. Research assistants may also help with literature reviews, code development, and documentation. The position is often found in academic, industry, or research lab settings, and usually requires a solid foundation in programming, mathematics, and neural network concepts.

What are the key skills and qualifications needed to thrive as a research assistant deep learning?

To thrive as a Research Assistant in Deep Learning, you need a strong background in machine learning, programming (especially Python), and a relevant degree in computer science or a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, as well as experience with data preprocessing and GPU computing, are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills and qualities are essential for efficiently developing, testing, and improving advanced machine learning models in a fast-evolving field.

What does a research assistant deep learning do?

As a Research Assistant in Deep Learning, you can expect to work closely with research scientists and engineers to design, implement, and evaluate novel deep learning models. Typical daily tasks include data preprocessing, running experiments, analyzing results, and contributing to academic papers or presentations. You may also assist in developing codebases, conducting literature reviews, and collaborating with team members to solve technical challenges. The work environment is often collaborative and fast-paced, with opportunities to learn from experts and contribute to cutting-edge research projects.

What are popular job titles related to Research Assistant Deep Learning jobs in Maryland?

For Research Assistant Deep Learning jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Research Assistant Deep Learning jobs in Maryland look for?

The top searched job categories for Research Assistant Deep Learning jobs in Maryland are:

What cities in Maryland are hiring for Research Assistant Deep Learning jobs?

Cities in Maryland with the most Research Assistant Deep Learning job openings:

Research Assistant Deep Learning jobs near you
Infographic showing various Research Assistant Deep Learning job openings in Maryland as of August 2026, with employment types broken down into 1% As Needed, 68% Full Time, 28% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Staff Deep Learning Engineer

Quidient

Columbia, MD • On-site

$185K - $235K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 28 days ago

Job description

Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world's great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything. Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First.
Overview
We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient's GSR platform. This is not an applied-ML role - you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.
This is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.
What You'll Do
Research & Network Design
  • Design and train novel deep neural network architectures from scratch for a variety of reconstruction tasks - including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.
  • Implement state-of-the-art papers and adapt published architectures to Quidient's specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production.
  • Identify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.
  • Design and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.
Model Development
  • Run end-to-end experiments independently - from hypothesis through data preparation, training, evaluation, and iteration - with minimal supervision.
  • Stay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.
  • Contribute production-quality C++ and Python to integrate trained models into the reconstruction engine.
  • Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM
  • Drive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.
What You Bring
Must-Have Qualifications:
  • Master's or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience.
  • 6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.
  • Deep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.
  • Ability to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.
  • Strong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.
  • Willingness to work on-site in Columbia, MD, in a hybrid capacity.
  • Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification
Nice-to-Have Qualifications:
  • Experience in fast-paced or startup environments.
  • Publications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).
  • Experience designing evaluation pipelines and experiment infrastructure for deep learning research.
  • Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.
  • Track record of taking a research idea from paper to production-deployed model.
What We Offer
Compensation:
  • Salary Range: $185,000 - $235,000.
  • Annual bonus and equity as appropriate.
Benefits:
  • Health insurance
  • HSA
  • 401(k) with company match
  • Life & disability insurance
  • Paid holidays & generous PTO
  • Opportunities for bonuses, equity, and career growth
Equal Opportunity Employer Statement
Quidient is an Equal Opportunity Employer. Quidient will consider all qualified applicants without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other classification protected by applicable state, federal, or local laws.

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